wavelet

说明：  算谐振频率的一个小程序，并结果附图，很有参考价值(Operator resonant frequency of a small program, and the results of drawings, of great reference value to)

说明：  clpso,pso的改进版本，又名学习策略pso(clpso, pso improved version, also known as learning strategies pso)

This is a series of function that deals with setting & getting the hotplug resource table in some environment variable.

This is a book about coding in SIMULINK matlab

低通滤波器，每句程序都有注释，易懂，还有一个实例，便于加深认识(Low-pass filter, each sentence programs have notes, easy to understand, there is an instance, to facilitate a better understanding of)

曲柄滑块机构的运动学仿真分析，分析各个杆的速度、位移、加速度曲线，以及曲柄滑块机构的动画。(Kinematics simulation analysis of the slider-crank mechanism)

《MATLAB语言常用算法程序集》源程序，很全的数值算法(good for study)

说明：  matlab牛顿迭代方法,上机试验对于初学者又很大的帮助,简单易懂(Matlab Newton iterative method, the pilot on the plane for beginners also very helpful and easily understood)

：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多 特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究， 提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一 种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。 为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明， 该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。(: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.)

包含不同条件下的MAB问题 仿真，还有仿真结果图例(include MAB PROBLEM policyBayesUCB.m policyCPUCB.m policyDMED.m policyGittins.m policyKLUCB.m )